Washing machine for adjusting operation based on injected detergent and method for controlling the same

Patent metadata

Field	Value
Publication number	US-11149376-B2
Application number	US-201916665436-A
Country	US
Kind code	B2
Filing date	Oct 28, 2019
Priority date	Sep 2, 2019
Publication date	Oct 19, 2021
Grant date	Oct 19, 2021

Abstract

A washing machine capable of operating in an IoT environment through a 5G communication network and estimating the type and amount of injected detergent through a neural network model created according to machine learning, and a method of controlling the washing machine, are provided. The washing machine may include a first tub into which laundry is loaded, a water supplier configured to supply washing water to the first tub, a detergent detection sensor configured to detect first conductivity and first turbidity of the washing water, and a processor configured to determine a washing cycle of the washing machine based on detected information.

First claim

What is claimed is:

1. A washing machine comprising:
a first tub into which laundry is loaded;
a water supplier configured to supply washing water to the first tub;
a detergent detection sensor configured to detect a first conductivity and a first turbidity of the washing water after the washing water is supplied to the first tub; and
a processor configured to determine a first washing cycle for the washing machine based on an amount of laundry loaded into the first tub,
wherein the processor is further configured to determine a type and amount of detergent injected in the washing water based on the detected first conductivity and first turbidity of the washing water, and determine a second washing cycle for the washing machine based on the determined type and amount of the detergent.

2. The washing machine of claim 1 , wherein the detergent detection sensor is further configured to detect a first temperature of the washing water, and correct the first conductivity and the first turbidity of the washing water based on the detected first temperature, and
wherein the processor is further configured to determine the type and amount of the detergent injected in the washing water based on the corrected first conductivity and first turbidity of the washing water.

3. The washing machine of claim 1 , further comprising a weight sensor configured to detect the amount of the laundry loaded into the first tub.

4. The washing machine of claim 3 , wherein the processor is configured to generate a signal that requests additional detergent based on the determined amount of the detergent being less than a threshold,
wherein the threshold varies based on the amount of the laundry detected by the weight sensor.

5. The washing machine of claim 1 , further comprising a second tub configured to accommodate the first tub,
wherein the first tub has an opening on its surface and is rotatably coupled to the second tub, and
wherein the detergent detection sensor penetrates through the second tub to contact the washing water in the second tub.

6. The washing machine of claim 2 , wherein the determining of the type and amount of the detergent injected in the washing water is performed by applying the detected first conductivity and first turbidity to a first neural network model, and
wherein the first neural network model has been previously trained using training data that comprises conductivity and turbidity measured in a solution, and a type and an amount of detergent that should be injected in the solution.

7. The washing machine of claim 2 , wherein the processor is further configured to determine a rinsing cycle based on the determined type of detergent.

8. The washing machine of claim 7 , wherein the detergent detection sensor is further configured to detect a second conductivity and a second turbidity of rinsing water during the rinsing cycle, and
wherein the processor is further configured to estimate a state of the rinsing water based on the detected second conductivity and second turbidity, and to adjust the rinsing cycle based on the estimated state of the rinsing water.

9. The washing machine of claim 8 , wherein the detergent detection sensor is further configured to detect a third conductivity and a third turbidity of the rinsing water during the adjusted rinsing cycle, and
wherein the processor is further configured to determine a rinsing performance based on a difference between the second conductivity and the third conductivity of the rinsing water, and a difference between the second turbidity and the third turbidity of the rinsing water.

10. The washing machine of claim 9 , wherein the processor is further configured to, after the rinsing cycle is completed:
estimate a degree of rinsing based on the determined rinsing performance; and
transmit information of the estimated degree of rinsing to a user terminal, and adjust the rinsing cycle based on a signal received from the user terminal in response to the transmitted information.

11. The washing machine of claim 1 , wherein the detergent detection sensor is configured to detect the first conductivity and the first turbidity of the washing water immediately after the washing water is supplied to the first tub.

12. A method of controlling a washing machine, the method comprising:
detecting an amount of laundry loaded into a first tub of the washing machine;
supplying washing water to the first tub;
determining, by a processor, a first washing cycle for the washing machine based on the detected amount of laundry;
detecting, by a detergent detection sensor, a first conductivity and a first turbidity of the washing water after the washing water is supplied to the first tub;
determining, by the processor, a type and amount of detergent injected in the washing water based on the detected first conductivity and first turbidity of the washing water; and
determining, by the processor, a second washing cycle for the washing machine based on the determined type and amount of the detergent.

13. The method of claim 12 , further comprising:
detecting a first temperature of the washing water; and
correcting the first conductivity and the first turbidity of the washing water based on the detected first temperature,
wherein the determining of the type and amount of the detergent injected in the washing water comprises determining the type and amount of the detergent injected in the washing water based on the corrected first conductivity and the first turbidity of the washing water.

14. The method of claim 12 , further comprising generating a signal that requests additional detergent based on the determined amount of the detergent being less than a threshold,
wherein the threshold varies based on the amount of the laundry.

15. The method of claim 12 , wherein the determining of the type and amount of the detergent injected in the washing water is performed by applying the detected first conductivity and first turbidity to a first neural network model, and
wherein the first neural network model has been previously trained using training data that comprises conductivity and turbidity measured in a solution, and a type and an amount of detergent injected in the solution.

16. The method of claim 12 , further comprising:
draining the washing water; and
determining a rinsing cycle based on the type of the detergent.

17. The method of claim 16 , further comprising, after the determining of the rinsing cycle:
supplying rinsing water into the first tub;
detecting a second conductivity and a second turbidity of the rinsing water during the rinsing cycle;
estimating a state of the rinsing water based on the detected second conductivity and second turbidity; and
adjusting the rinsing cycle based on the estimated state of the rinsing water.

18. The method of claim 17 , further comprising:
detecting a third conductivity and a third turbidity of the rinsing water during the adjusted rinsing cycle; and
determining a rinsing performance based on a difference between the second conductivity and the third conductivity of the rinsing water, and a difference between the second turbidity and the third turbidity of the rinsing water.

19. The method of claim 18 , further comprising:
estimating a degree of rinsing based on the determined rinsing performance;
transmitting information of the estimated degree of rinsing to a user terminal; and
adjusting the rinsing cycle based on a signal received from the user terminal in response to the transmitted information.

Assignees

• Lg Electronics Inc

Inventors

• Cha Seung Chul
• Koo Bon Kwon
• Park Hyun Ji
• Lee Sang Hyun

Classifications

• D06F2105/60

  Audible signals · CPC title

• D06F2105/58

  Indications or alarms to the control system or to the user · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/09

  Supervised learning · CPC title